The Role of Feedback Alignment in Self-Distillation
Researchers demonstrate that self-distillation in language models improves significantly when feedback is structurally aligned with the model's reasoning trace rather than using binary rewards or reference solutions. Step-aligned critique, which targets only tokens where reasoning fails, outperforms alternative approaches by 5-16 points, suggesting that feedback design fundamentally shapes model learning efficiency.